This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin

This study investigates the possibility of removing ciprofloxacin (CIP) using three types of adsorbent based on green-prepared iron nanoparticles (Fe.NPs), copper nanoparticles (Cu. NPS), and silver nanoparticles (Ag. NPS) from synthesized aqueous solution. They were characterized using different analysis methods. According to the characterization findings, each prepared NPs has the shape of a sphere and with ranges in sizes from of 85, 47, and 32 nanometers and a surface area of 2.1913, 1.6562, and 1.2387 m²/g for Fe.NPs, Cu.NPs and Ag.NPs, respectively. The effects of various parameters such as pH, initial CIP concentration, temperature, NPs dosage, and time on CIP removal were investigated through batch experiments. The res

In this research a local adsorbent was prepared from waste tires using two-step pyrolysis method. In the carbonization process, nitrogen gas flow rate was 0.2L/min at carbonization temperature of 500ºC for 1h. The char products were then preceded to the activation process at 850°C under carbon dioxide (CO2) activation flow rate of 0.6L/min for 3h. The activation method produced local adsorbent material with a surface area and total pore volume as high as 118.59m2 /g and 0.1467cm3/g, respectively. The produced . local adsorbent (activated carbon) was used for adsorption of lead from aqueous solution. The continuous fixed bed column experiments were conducted. The adsorption capacity performance of prepared activated carbons in this work

The adsorption of Pb(II) ions onto bentonite and activated carbon was investigated. The effects of pH, initial adsorbent dosage, contact time and temperature were studied in batch experiments. The maximum adsorption capacities for bentonite and activated carbon were 0.0364 and 0.015 mg/mg, respectively. Thermodynamic parameters such as Gibbs free energy change, Enthalpy change and Entropy change have been calculated. These thermodynamic parameters indicated that the adsorption process was thermodynamically spontaneous under natural conditions and the adsorption was endothermic in nature. Experimental data were also tested in terms of adsorption kinetics, the results showed that the adsorption processes followed well pseudo second- order

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

In drilling processes, the rheological properties pointed to the nature of the run-off and the composition of the drilling mud. Drilling mud performance can be assessed for solving the problems of the hole cleaning, fluid management, and hydraulics controls. The rheology factors are typically termed through the following parameters: Yield Point (Yp) and Plastic Viscosity (μp). The relation of (YP/ μp) is used for measuring of levelling for flow. High YP/ μp percentages are responsible for well cuttings transportation through laminar flow. The adequate values of (YP/ μp) are between 0 to 1 for the rheological models which used in drilling. This is what appeared in most of the models that were used in this study. The pressure loss